Cryptocurrency Comparative Analysis: BTC vs ETH

A data-driven comparison of Bitcoin (BTC) and Ethereum (ETH) across three dimensions: trend analysis, risk profile assessment, and correlation analysis.

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Project Structure

comparative_analysis.ipynb   # Main analysis notebook
crypto_data_20251019_142608.csv   # Source dataset (required)
README.md                    # This file
Reports.md                   # Full findings and insights

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Dataset

Property	Value
Source file	crypto_data_20251019_142608.csv
Date range	October 20, 2024 – October 19, 2025
Records	732 rows (366 per coin)
Coins	Bitcoin (bitcoin), Ethereum (ethereum)

Columns

Column	Description
date	Trading date
coin	Asset identifier (bitcoin or ethereum)
price	Daily closing price (USD)
volume	Daily trading volume (USD)
market_cap	Market capitalisation (USD)
daily_return	Day-over-day percentage return
ma_7	7-day moving average of price
ma_30	30-day moving average of price
volatility_30d	30-day rolling standard deviation of returns
cumulative_return	Cumulative return since start of period

Note on missing values: daily_return and cumulative_return are NaN for the first row of each coin (no prior day). volatility_30d is NaN for the first ~30 rows. These are expected for time-series calculations and are handled via .dropna() throughout.

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Analysis Structure

Part A — Trend Analysis

Compares price performance, moving averages, and trading volume between the two assets.

• A1: Basic price statistics (mean, median, min, max, total return)
• A2: Raw price trend visualisation
• A3: Normalised price comparison (base 100)
• A4: Moving average analysis (7-day and 30-day MA with trend signals)
• A5: Volume analysis and comparison

Part B — Risk Profile Assessment

Quantifies the risk characteristics of each asset.

• B1: Daily returns distribution (skewness, kurtosis, range)
• B2: Volatility analysis (daily, annualised, rolling 30-day)
• B3: Drawdown analysis (maximum drawdown, current drawdown, longest recovery)
• B4: Value at Risk — VaR and CVaR at 95% confidence
• B5: Risk-adjusted returns (Sharpe Ratio, Sortino Ratio, win rate)

Part C — Correlation Analysis

Examines the relationship between BTC and ETH returns.

• C1: Data preparation (date-aligned merged return series)
• C2: Pearson and Spearman correlation coefficients
• C3: Scatter plot with regression line
• C4: 30-day rolling correlation
• C5: Correlation by market condition (bull, bear, mixed days)
• C6: Lead-lag relationship analysis (up to ±5 days)
• C7: Cumulative returns comparison ($1 invested)
• C8: Monthly correlation breakdown

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Dependencies

pip install pandas numpy matplotlib seaborn scipy

Library	Purpose
pandas	Data loading, transformation, grouping
numpy	Numerical calculations (VaR, annualisation)
matplotlib	All chart generation
seaborn	Styling and palette
scipy.stats	Pearson/Spearman correlation, skewness, kurtosis

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Running the Notebook

1. Update the path in Cell 2.
2. Install dependencies listed above.
3. Run all cells sequentially — cells are designed to execute top-to-bottom.

All charts display inline. No files are written to disk by the notebook itself.

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Key Findings Summary

Metric	Bitcoin	Ethereum
Total Return	+58.49%	+50.61%
Annualised Volatility	44.49%	76.83%
Maximum Drawdown	-28.12%	-63.36%
Sharpe Ratio	1.256	0.912
Sortino Ratio	1.942	1.445
95% VaR (daily)	-3.39%	-5.62%

Overall correlation (Pearson): 0.7792 — strong positive, statistically significant.

See report.md for the full analysis narrative and interpretation.